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7.8. Pairwise metrics, Affinities and Kernels —...
1. , 0.51341712, 0.83822343], [0.89483932, 0.51341712, 1. ,...array([[1. , 0.36787944, 0.89483932, 0.58364548], [0.36787944,...scikit-learn.org/stable/modules/metrics.html -
1.9. Naive Bayes — scikit-learn 1.8.0 documenta...
= P(x_i = 1 \mid y) x_i + (1 - P(x_i = 1 \mid y)) (1 - x_i)\]...\[P(x_i | y, x_1, \dots, x_{i-1}, x_{i+1}, \dots, x_n) = P(x_i | y),\]...scikit-learn.org/stable/modules/naive_bayes.html -
2.9. Neural network models (unsupervised) — sci...
\[\sigma(x) = \frac{1}{1 + e^{-x}}\] 2.9.1.3. Stochastic Maximum...binary values or values between 0 and 1, each encoding the probability...scikit-learn.org/stable/modules/neural_networks_unsupervised.html -
Scaling the regularization parameter for SVCs —...
- 1.3 , 10 ) train_sizes = np . linspace ( 0.3 , 0.7 , 3...alpha = 0.7 ) axes [ 1 ] . set_title ( "Scaling C by sqrt(1 / n_samples)"...scikit-learn.org/stable/auto_examples/svm/plot_svm_scale_c.html -
Decision boundary of semi-supervised classifier...
Manual: [[0.96 0.03 0.01]] API : [[0.96 0.03 0.01]] predict_proba...= ( 0.5 , 0.0 ) ) fig . tight_layout ( rect = [ 0 , 0.03 , 1...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
Multilabel classification — scikit-learn 1.8.0 ...
subplots_adjust ( 0.04 , 0.02 , 0.97 , 0.94 , 0.09 , 0.2 ) plt . show...w = clf . coef_ [ 0 ] a = - w [ 0 ] / w [ 1 ] xx = np . linspace...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multilabel.html -
Comparing randomized search and grid search for...
with rank: 1 Mean validation score: 0.993 (std: 0.007) Parameters:...score: 0.983 (std: 0.018) Parameters: {'alpha': np.float64(0.29696409452126926),...scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html -
sklearn.preprocessing — scikit-learn 1.8.0 docu...
data (set feature values to 0 or 1) according to a threshold....labels with value between 0 and n_classes-1. MaxAbsScaler Scale each...scikit-learn.org/stable/api/sklearn.preprocessing.html -
Pipelining: chaining a PCA and a logistic regre...
LogisticRegression ( max_iter = 10000 , tol = 0.1 ) pipe = Pipeline ( steps...( np . arange ( 1 , pca . n_components_ + 1 ), pca . explained_variance_ratio_...scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html -
7.6. Random Projection — scikit-learn 1.8.0 doc...
eps = [ 0.5 , 0.1 , 0.01 ]) array([ 663, 11841,...}}} & & 1 / 2s\\ 0 &\text{with probability} & 1 - 1 / s \\ +...scikit-learn.org/stable/modules/random_projection.html